Project Summary The overall goal of this project is to develop a new diagnostic tool, called Cyst-X, for accurate detection and characterization of pre-cancerous pancreatic cysts and improve patient outcome through precise decisions (surgical resection or surveillance). Pancreatic cancer is the most fatal cancer among all cancers due to its poor prognosis and lack of early detection methods. Unlike other common cancers where precursor lesions are well known (colon polyps-colon cancer, ductal carcinoma in situ (DCIS)-breast cancer), pancreas cancer precursors (cysts) are poorly understood. Diagnosing pancreatic cancer at earlier stages may decrease mortality and morbidity rates of this lethal disease. One major approach for diagnosing pancreatic cancer at earlier stages is to target pancreatic precancerous pancreatic neoplasms (cysts) before they turn into invasive cancer. Once cysts are detected with radiology imaging such as magnetic resonance imaging (MRI), they should be characterized with respect to their malignant potential. Low-risk cysts remain harmless; hence, patients should remain under surveillance program. On the other hand, high-risk cysts can progress into an aggressive cancer, therefore, patients should undergo surgical resection if possible. Despite this, international guidelines for risk stratification of pancreatic cysts are woefully deficient (55-76% accuracy for determining characteristics of low-risk vs high risk cystic tumors, while only 40-50% accuracy detecting cysts with MRI). Combined, these critical barriers indicate that there is an urgent need for improving characterization of pancreatic cystic tumors. Based on our preliminary results, which support the development of an image-based diagnostic decision tool, we hypothesize that our proposed Cyst-X will produce higher diagnostic accuracy for characterizing pancreatic cysts and provide better patient management compared to the current guidelines. Towards this overarching hypothesis, we will first use powerful deep learning methods (specifically deep capsule networks) for automatically detecting and segmenting the pancreas and pancreatic cysts from multi-sequence MRI scans (Aim 1). Next, we will create an interpretable image-based diagnosis model for characterizing pancreatic cysts (Aim 2). Accurate characterization is necessary for such a diagnostic model; however, emphasis will also be placed on interpretability of the machine generated diagnostic model. Visual explanation of the discriminative features will help radiologists obtain higher decision rates in patient management. In Aim 3, we will validate the proposed Cyst-X framework in a multi-center study. A total of 1200 multi-sequence MRI scans will be collected from three participating clinical centers (Mayo Clinic, Columbia University Medical Center, Erasmus Medical Center). Comprehensive evaluations will be made to test the validity and generalizability of Cyst-X. All evaluations will be made with respect to the international guidelines and biopsy proven ground truths. Our proposed study has wide implications: specifically, in the long term, it will influence early diagnosis of pancreatic cancer and clinical decision making to improve survival rates of pancreatic cancer.